First, the bias may come from the data themselves, in the sense that the distribution of the training data is not the ideal distribution that would reflect the desired behavior that we want to learn. Compared with tabular data, image datasets can be difficult to collect, store and manipulate due to their considerable size and the memory storage they require. Hence, many of them have proven to lack diversity – e.g. because not all regions are studied (geographic diversity), or not all sub-population samples are uniformly collected (gender or racial diversity). The growing use of facial recognition algorithms in a wide range of areas affecting our society is currently debated. Indeed, they have demonstrated to be a source of racial [1]}, [2]}, or gender [3]} discrimination. Besides, well-known datasets such as CelebA [4]}, Open Images [5]} or ImageNet [6]} lack of diversity – as shown in [7]} or [8]} – resulting in imbalanced samples. Thus, state-of-the-art algorithms are unable to yield uniform performance over all sub-populations. A similar lack of diversity appear in the newly created Metaverse as pointed out in [9]} creating racial bias. This encouraged several researchers to design datasets that do not suffer from these drawbacks – i.e. preserving diversity – as illustrated by the Pilot Parliament Benchmark (PPB) dataset [10]} or in [11]} or in Fairface dataset [12]}.
